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结合 LSTM 网络与已实现 GARCH 预测波动率

文章 arXiv papers · 作者: Chen Liu et al.
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总结

该框架将已实现波动率指标与经 LSTM 增强的已实现 GARCH 模型结合，以共同刻画收益和已实现波动率。它使用序贯蒙特卡洛进行贝叶斯推断和预测，融合了金融计量经济学建模、高频数据与深度学习。研究评估了该框架在波动率预测、尾部风险预测和期权定价方面的表现，并以边际似然衡量模型拟合度。

实证分析涵盖 31 个交易活跃的股票指数，研究时段包括 COVID-19 疫情。作者报告称，该模型样本内拟合度较高，预测表现优于若干基准模型，并能适应常见的波动率特征。描述未说明基准模型或提供指标数值，因此仅凭这份说明无法评估所报告优势的幅度和稳健性。

核心观点

  • 该模型采用经 LSTM 增强的已实现 GARCH 结构，共同刻画收益与已实现波动率。
  • 贝叶斯推断和预测使用序贯蒙特卡洛。
  • 评估范围包括波动率预测、尾部风险、期权定价和边际似然。
  • 实证研究使用 31 个股票指数，并报告相较若干未说明基准模型的优势。

标签

波动率机器学习统计学期权
全文
# Deep Learning Enhanced Realized GARCH


# Deep Learning Enhanced Realized GARCH









We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and distills modeling advances from financial econometrics, high frequency trading data and deep learning. Bayesian inference via the Sequential Monte Carlo method is employed for statistical inference and forecasting. The new framework can jointly model the returns and realized volatility measures, has an excellent in-sample fit and superior predictive performance compared to several benchmark models, while being able to adapt well to the stylized facts in volatility. The performance of the new framework is tested using a wide range of metrics, from marginal likelihood, volatility forecasting, to tail risk forecasting and option pricing. We report on a comprehensive empirical study using 31 widely traded stock indices over a time period that includes COVID-19 pandemic.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。